Tracking data management using topology of moving object database system for moving object constrained trajectory in LBS application

Advances in wireless technologies and positioning technologies and spread of wireless devices, an interest in LBS (Location Based Service) is arising. To provide location based service, tracking data should have been stored in moving object database management system (called MODBMS) with proper policies and managed efficiently. So the methods which acquire the location information at regular time intervals then, store and manage have been studied. In this paper, we suggest tracking data management techniques using topology that is corresponding to the moving path of moving object. In our techniques, we update the MODBMS when moving object arrived at a street intersection or a curved road which is represented as the node in topology and predict the location at past and future with attribute of topology and linear function. In this technique, location data that are corresponding to the node in topology are stored, thus reduce the number of update and amount of data. Also in case predicting the location, because topology are used as well as existing location information, accuracy for prediction is increased than applying linear function or spline function.     

视频序列中运动目标跟踪新方法

提出了一种跟踪视频图像序列中运动目标的新方法.该方法利用一种基于动态信息窗口的自适应背景更新方法解决背景的复杂性问题,结合一种新的计算颜色模型解决运动阴影问题,从而得到具有精确边缘的特定运动目标.计算了运动目标灰度质心,在坐标系中记录其位置,并采用最小二乘法拟合实现了对运动目标的跟踪.实验结果表明这种方法能有效地跟踪并预测视频序列中的运动目标.     

基于DSPs的运动目标检测与匹配

建立了一个基于TMS320C6701的DSPs系统,实现复杂背景中单个运动目标的实时跟踪.同时给出在复杂背景下人物自适应跟踪的实验结果.在这基础上,结合运动目标检测技术和模板匹配法,讨论一个复杂背景下多运动目标自适应跟踪系统,同时也给出运动目标检测和模板匹配的实验性结果.     

一种改进的Hausdorff距离目标跟踪算法

在序列图像中进行目标跟踪是计算机视觉、图像处理和模式识别领域里非常活跃的课题。采用Hausdorff距离模板匹配的方法具有计算量小、适应性强的特点,为了能对复杂背景（包括运动背景或移动镜头）情况下的序列图像进行准确的跟踪,综合考虑了图像边缘的位置信息和方向信息,对模板匹配和模板更新的策略作了改进,与原有方法相比,目标跟踪的准确度和算法的效率有了显著提高。     

一种快速运动目标检测算法

运动目标检测是智能视觉监控系统的重要组成部分,其主要功能是检测监控场景中的运动目标,为高层运动分析提供必要的信息。文章提出一种快速运动目标检测算法,以帧差法和背景减法为基础,快速实现背景提取、背景更新、运动目标检测的功能。实验结果表明,该算法计算量小,检测目标完整,能够满足实时监控系统的要求。     

Density-based clustering method in the moving object database

With the rapid advance of wireless communication, tracking the positions of the moving objects is becoming increasingly feasible and necessary. Because a large number of people use mobile phones, we must handle a large moving object database as well as the following problems. How can we provide the customers with high quality service, that means, how can we deal with so many enquiries within as less time as possible? Because of the large number of data, the gap between CPU speed and the size of main memory has increasing considerably. One way to reduce the time to handle enquiries is to reduce the I/O number between the buffer and the secondary storage. An effective clustering of the objects can minimize the I/O-cost between them. In this paper, according to the characteristic of the moving object database, we analyze the objects in buffer, according to their mappings in the two-dimension coordinate, and then develop a density-based clustering method to effectively reorganize the clusters. This new mechanism leads to the less cost of the I/O operation and the more efficient response to enquiries.     

Density-based clustering method in the moving object database

With the rapid advance of wireless communication, tracking the positions of the moving objects is becoming increasingly feasible and necessary. Because a large number of people use mobile phones, we must handle a large moving object database as well as the following problems. How can we provide the customers with high quality service, that means, how can we deal with so many enquiries within as less time as possible? Because of the large number of data, the gap between CPU speed and the size of main memory has increasing considerably. One way to reduce the time to handle enquiries is to reduce the I/O number between the buffer and the secondary storage. An effective clustering of the objects can minimize the I/O cost between them. In this paper, according to the characteristic of the moving object database, we analyze the objects in buffer, according to their mappiigs in the two-dimension coordiiate, and then develop a density-based clustering method to effectively reorganize the clusters. This new mechanism leads to the less cost of the I/O operation and the more efficient response to enquiries.     

Density-based clustering method in the moving object database

With the rapid advance of wireless communication, tracking the positions of the moving objects is becoming increasingly feasible and necessary. Because a large number of people use mobile phones, we must handle a large moving object database as well as the following problems. How can we provide the customers with high quality service, that means, how can we deal with so many enquiries within as less time as possible? Because of the large number of data, the gap between CPU speed and the size of main memory has increasing considerably. One way to reduce the time to handle enquiries is to reduce the I/O number between the buffer and the secondary storage. An effective clustering of the objects can minimize the I/O-cost between them. In this paper, according to the characteristic of the moving object database, we analyze the objects in buffer, according to their mappings in the two-dimension coordinate, and then develop a density-based clustering method to effectively reorganize the clusters. This new mechanism leads to the less cost of the I/O operation and the more efficient response to enquiries.     

一种运动目标自适应检测与跟踪算法

提出一种运动目标自适应检测、跟踪算法。该算法利用自适应运动的检测算法，实现目标运动判断，减少了系统程序的计算量。根据图像处理提取的目标特征，采用卡尔曼滤波器，跟踪预测目标的搜索区域，从而建立每个目标的“目标链”，快速实现多目标的数据关联。实验证明，该算法保证了多目标跟踪的准确性、稳定性和连续性。     

基于LoRa的物联网数据传输系统研究与设计

提出了一种基于LoRa技术的物联网数据传输系统.以物联网中广泛使用的无线传感器网络的数据传输实际需求为研究对象,综合考虑传输系统实际工作环境及经济成本,传输距离,可靠性等问题,设计出一种以LoRa无线传输技术构成的星型网络,能为用户提供一种简单的能实现远距离、易扩展的数据传输系统.介绍了系统网络拓扑结构、系统软硬件设计和自定义通信协议的设计过程.其中,LoRa终端节点内嵌Contiki嵌入式专用操作系统,有效减少用户定义多线程多任务对内存的消耗,节点采用循环队列缓冲的数据通信,具有封装成帧、确认回复、周期性休眠等功能.异构网关负责存储转发并且支持NB-IoT,WiFi等多种通信技术上传至远端云平台,在远端云平台可以配置并显示终端节点信息.对系统进行了功能和性能测试,达到了预期目标,具有一定的应用价值.     

基于模板匹配的运动目标快速检测与跟踪

在图像跟踪系统中，运动目标的快速检测与识别是至关重要的．通过分析视频序列图像的特点，并结合帧差法和多分辨率图像匹配的优点，提出了一种基于模板匹配的快速检测识别目标的方案．实验结果表明，该方法能快速有效地识别目标，基本上达到了实时跟踪的要求．     

基于RFID位置语义的室内移动轨迹聚类算法

面向室内空间的移动轨迹聚类有利于发现室内热点和用户移动模式.针对室内环境在定位技术、距离度量等方面的特殊性,充分考虑室内移动轨迹的空间和语义特征,提出一种基于无线射频识别(radio frequency identi-fication,RFID)位置语义的室内移动轨迹聚类方法.该方法对原始轨迹提取特征点,可简化轨迹以降低算法时间复杂度;从空间形状和位置语义2个方面加权计算轨迹相似度,其中,空间相似度通过定义适用于室内三维空间的距离函数来计算,语义相似度计算基于最长公共子序列思想,并引入移动对象在轨迹点的到达时间和停留时间;利用线性表存储轨迹相似度,采用改进的层次聚类方法对移动轨迹进行聚类.实验结果表明,该方法能够有效地进行室内轨迹聚类并具有较高的效率.     

序列图像运动目标检测的一种快速算法

研究了序列视频图像中运动目标的检测与跟踪快速算法．研究基于Kalman滤波理论的渐消记忆最小二乘法，用该方法重建背景图像；采用图像差分算法提取运动目标；提出简化的等效灰度投影算法来计算目标的质心；采用记忆外推跟踪算法实现图像目标的跟踪，并且对全部算法做了仿真．仿真结果表明算法简单、有效、执行速度快、具有很强的适应性，能够用于单镜头序列图像中运动目标的检测与跟踪．     

Distributed location-based query processing on large volumes of moving items

Recently, new techniques to efficiently manage current and past location information of moving objects have received significant interests in the area of moving object databases and location-based service systems. In this paper, we exploit query processing schemes for location management systems, which consist of multiple data processing nodes to handle massive volume of moving objects such as cellular phone users.To show the usefulness of the proposed schemes, some experimental results showing performance factors regarding distributed query processing are explained. In our experiments, we use two kinds of data set: one is generated by the extended GSTD simulator and another is generated by the real-time data generator which generates location sensing reports of various types of users having different movement patterns.     

TMS320C6701在运动目标实时跟踪中的应用

描述一种使用高速图像处理器和摄像头进行运动目标实时跟踪的系统 .图像处理器的核心由一块TMS32 0C6 70 1数字信号处理板及图像实时采集卡构成 .在对采集的初始图像序列作若干预处理后 ,采用改进的加约束的光流场算法从复杂背景中快速地计算出光流场 .根据运动目标的形状特征 ,较为完整地提取出运动目标区域 ,并计算目标运动的速度大小和方向 ,最后根据这些参数控制摄像云台机构持续跟踪移动的目标 .文中还给出在复杂背景下跟踪人物的实验结果 .     

基于幂次变换和区域收缩算法的运动目标检测与定位

运动目标分割与跟踪是计算机视觉中的重要研究课题,而目标检测与定位是其中的必要步骤,对分割与跟踪效果影响很大.在此提出一种新的运动目标检测与定位方法,该方法在差分二值图像上,通过区域收缩定位到运动像素密度较大的区域,从而实现运动目标定位.在多目标情况下,先通过幂次变换突出不同位置的目标,然后再通过区域收缩实现目标定位,给出目标的特征矩形,便于进一步的跟踪与识别.该方法不需要任何关于目标数的先验知识,对噪声鲁棒性较强.文中给出的实验结果证明该算法的有效性.     

Distributed location-based query processing on large volumes of moving items

Recently, new techniques to efficiently manage current and past location information of moving objects have received significant interests in the area of moving object databases and location-based service systems. In this paper, we exploit query processing schemes for location management systems, which consist of multiple data processing nodes to handle massive volume of moving objects such as cellular phone users. To show the usefulness of the proposed schemes, some experimental results showing performance factors regarding distributed query processing are explained. In our experiments, we use two kinds of data set: one is generated by the extended GSTD simulator and another is generated by the real-time data generator which generates location sensing reports of various types of users having different movement patterns.     

基于移动预测的DSR路由缓存管理改进

Ad Hoc网络动态的拓扑结构变化使得路由经常失效从而造成网络性能的下降,本文对DSR(Dynamic Source Routing) 由协议的缓存管理进行了研究,提出了通过GPS(GlobalPositioning System)系统获得移动结点的位置及速度信息,预测路由的生存期,并基于该生存期对路由缓存进行管理,该算法可以提高缓存中路由信息的准确率,较好的适应了Ad Hoc网络动态的网络拓扑结构变化.通过仿真实验,验证了该方法可以有效提高分组投递率,同时降低路由开销及传输延迟.     

无线传感器网络中双层受限中继节点放置算法研究

针对在三维空间中,对于中继节点(RN)的位置受限并且是双层拓扑的情况,提出了一种基于混合整数线性规划的中继节点放置算法,该算法首先考虑三维空间中继节点放置的物理层模型,然后基于混合整数线性规划(MIPS)给出最优能效的分簇,使得每个传感器节点与相应簇头之间的传输距离最小.仿真结果表明:与只考虑最小化簇内距离的中继节点放置算法相比,本算法在降低重传率和延长网络生命周期方面都有较大的改善.     

基于Kalman的运动物体跟踪和预测方法研究

目前,很多应用需要跟踪图像序列中的运动物体。但是,有时不知道运动物体的特性,因此,提出一个完整的跟踪预测模型;使用无需先验知识的Kalman滤波器跟踪和预测运动物体。利用提取的Harris角点,通过L-K金字塔方法得到前后两帧光流;通过光流聚类得到当前帧中运动物体的凸包,使运动物体从背景中分离出来。由Kalman滤波器跟踪和预测各运动物体凸包的重心,并划出运动轨迹。计算机仿真及现场测试结果表明所提出的方法具有较高的跟踪精度,且计算量小。